Syllabus Data Modeling and Visualization - (417522) Credit Examination Scheme : 03 In-Sem (Paper) : 30 Marks End-Sem (Paper) : 70 Marks Unit I Introduction to Data Modeling Basic probability : Discrete and continuous random variables, independence, covariance, central limit theorem, Chebyshev inequality, diverse continuous and discrete distributions. Statistics : Parameter Estimation, and Fitting a Distribution : Descriptive statistics, graphical statistics, method of moments, maximum likelihood estimation. Data Modeling Concepts • Understand and model subtypes and supertypes • Understand and model hierarchical data • Understand and model recursive relationships • Understand and model historical data. (Chapter - 1) Unit II Testing and Data Modeling Random Numbers and Simulation : Sampling of continuous distributions, Monte Carlo methods Hypothesis Testing : Type I and II errors, rejection regions; Z-test, T-test, F-test, Chi-Square test, Bayesian test. Stochastic Processes and Data Modeling : Markov process, Hidden Markov Models, Poisson Process, Gaussian Processes, Auto-Regressive and Moving average processes, Bayesian Network, Regression, Queuing systems. (Chapter - 2) Unit III Basics of Data Visualization Computational Statistics and Data Visualization : Types of Data Visualization, Presentation and Exploratory Graphics, Graphics and Computing, Statistical Historiography, Scientific. Design Choices in Data Visualization : Higher-dimensional Displays and Special Structures, Static Graphics : Complete Plots, Customization, Extensibility. Other Issues : 3-D Plots, Speed, Output Formats, Data Handling. (Chapter - 3) Unit IV Data Visualization and Data Wrangling Data Wrangling : Hierarchical Indexing, Combining and Merging Data Sets Reshaping and Pivoting. Data Visualization matplotlib : Basics of matplotlib, plotting with pandas and seaborn, other python visualization tools. Data Visualization Through Their Graph Representations : Data and Graphs Graph Layout Techniques, Force-directed Techniques Multidimensional Scaling, The Pulling Under Constraints Model, Bipartite Graphs. (Chapter - 4) Unit V Data Aggregation and Analysis Data Aggregation and Group operations : Group by Mechanics, Data aggregation, General split-apply-combine, Pivot tables and cross tabulation 67 Time Series. Data Analysis : Date and Time Data Types and Tools, Time series Basics, date Ranges, Frequencies and Shifting, Time Zone Handling, Periods and Periods Arithmetic, Resampling and Frequency conversion, Moving Window Functions. (Chapter - 5) Unit VI Data Analysis of Visualization and Modeling Reconstruction, Visualization and Analysis of Medical Images. Introduction : - PET Images, Ultrasound Images, Magnetic Resonance Images, Conclusion and Discussion, Case Study : ER/Studio, Erwin data modeler, DbSchema Pro, Archi, SQL Database Modeler, LucidChart, Pgmodeler. (Chapter - 6)
